Select a method for standardization using PROC STDIZE.Since variables with large variances tend to have more effect on the distance measure than those with small variances, it is recommended that you standardize the variables before the computation of the distance measure.
This option enables you to add a shift factor to the data without a normalization step. Standardizes values to zero (0) with a scale equivalent to the sum of the values. Standardizes values to zero (0) with a scale equivalent to the Euclidean length. Standardizes values to zero (0) with a scale equivalent to the standard deviation about the origin. Standardizes values to zero (0) with a scale equivalent to the maximum absolute value. Standardizes values to the biweight 1-step M -estimate with a scale equivalent to the biweight A -estimate.Note : 4.685 is the default numeric tuning constant used in this method. Standardizes values to the Huber 1-step M -estimate with a scale equivalent to the Huber A -estimate.Note : 1.345 is the default numeric tuning constant used in this method. Note : 1 is the default numeric tuning constant used in this method. Note : 0.1 is the default numeric constant giving the proportion of pairs to be included in the estimation of the within-cluster variances. Note : 0.1 is the default numeric constant giving the proportion of data to be contained in the spacing. Note : 0.1 is the default numeric constant specifying the power to which differences are to be raised in computing an L ( p ) or Minkowski metric.
• a _TYPE_ variable. The observation that contains the location measure corresponds to the value _TYPE_= ’LOCATION’ , and the observation that contains the scale measure corresponds to the value _TYPE_= ’SCALE’ .
Select the desired method from the Standardization Method drop-down list.